Physiological Signal Analysis Using User Feedback and Wavelet Transform
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Solution Overview
Problem
Current physiological signal monitoring technologies generate excessive data, leading to complexity and inaccuracies in diagnosis, as they rely solely on software and do not effectively integrate user opinions or hardware limitations, making it difficult for doctors to accurately diagnose patients outside of a hospital setting.
Innovation Solution
A method and system that combines physiological data with user opinions for analysis, utilizing AI and machine learning to reduce unnecessary data inputs and improve accuracy, by collecting and analyzing physiological signals such as EEG, ECoG, EKG, or EMG, through peak detection and wavelet transform processes, generating syndrome recognition parameters and weight parameters for real-time syndrome analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If long term monitoring is implemented to monitor patients outside hospital, then continuous physiological signal data can be obtained, but excessive data is generated that consumes storage space and confuses doctors
Solution Approach 1:
The patent extracts and analyzes only the characteristic values and key features from the continuous physiological signal data, rather than processing all raw data. This allows the system to maintain monitoring effectiveness while significantly reducing the volume of data that needs to be stored and reviewed by doctors.
Solution Approach 2:
The patent segments the continuous monitoring data into meaningful patterns and characteristics through signal processing techniques. By dividing the data into analyzable segments with specific features, the system reduces the overall data volume while preserving the essential information needed for diagnosis.
2Device complexity
If software-based physiological signal monitoring is used, then system complexity can be reduced, but hardware implementation limitations are not considered resulting in high algorithmic complexity
Solution Approach 1:
The patent changes the parameters of the analysis algorithm to be adaptable to different hardware capabilities. By making the algorithm parameters configurable and adjustable, the system can be implemented on various hardware platforms with different computational resources, reducing both system complexity and algorithmic complexity.
Solution Approach 2:
The patent introduces dynamic adaptability into the system, allowing the analysis methodology to adjust based on hardware capabilities and data characteristics. This dynamic approach enables the system to optimize its complexity level according to the specific implementation context.
3Productivity
If traditional analysis methods are used without user opinion integration, then analysis speed is maintained, but diagnosis accuracy is reduced due to lack of human determination
Solution Approach 1:
The patent implements feedback mechanisms where user opinions and expert determinations are incorporated into the analysis process. The system allows users to provide input that feeds back into the analysis, improving diagnosis accuracy while maintaining analysis speed through efficient processing of both automated and human inputs.
Solution Approach 2:
The patent introduces an intermediary layer that bridges automated signal analysis and human expert judgment. This intermediary process integrates user opinions with algorithmic results, enhancing diagnosis accuracy without significantly compromising analysis speed.
Data Source
AI summary
A method for physiological signal analysis and its system and a computer program product storing a physiological signal analysis program are provided. Physiological signals of a subject are collected for a user to provide a detection opinion for the physiological signals in order to generate syndrome recognition parameters and syndrome weight parameters such that the collected physiological signals are analyzed and determined. The invention performs detection determination by means of combining the physiological signals of the subject and referencing to an analysis opinion from the user. Therefore, an output detection result may be believed by both doctors and patients with effectively improved accuracy of analysis result to improve the efficiency of the user in diagnosis and treatment.